This article proposes a two-stage preprocessing framework that increases the verification performance of image-based biometric systems through image enhancement and deformation techniques.
In biometrics, quality refers to the intrinsic physical data content, which pertains to the accuracy with which physical characteristics are represented in a given biometric data. The performance of a biometric system depends on the quality of images collected as either a reference or a live sample. This article proposes a SVM-based algorithm that selects good quality local regions from different globally enhanced images and synergistically combines them to produce a high-quality, feature-rich image. The algorithm can be used to remove multiple irregularities present locally in the image without affecting the good quality regions. The authors also propose the phase congruency-based deformation correction algorithm that deforms the query image with respect to the reference image in order to minimize any variations between the two images. The proposed framework can be applied with any of the recognition algorithms in order to improve the verification accuracy. Validation of the framework involved the selection of face and iris images as the two case studies. The preprocessing framework improved the verification performance of face and iris images by 7.6 percent and 1.6 percent, respectively. 4 tables, 20 figures, and 52 references.